Update app.py

app.py

@@ -2,9 +2,9 @@ import gradio as gr
 from transformers import pipeline
 import torch
 
-# Load the physician and patient models via Hugging Face Model Hub
-physician = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B")  # Replace with actual medical model
-patient = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B")  # General conversational model
+# Load the smaller models
+physician = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B")
+patient = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B")
 
 def generate_conversation(topic, turns):
     conversation = []
@@ -14,20 +14,26 @@ def generate_conversation(topic, turns):
 
     # Initial prompt for the patient
     patient_prompt = f"I'm here to talk about {topic}."
+    print(f"Patient Initial Prompt: {patient_prompt}")  # Debugging
     patient_response = patient(patient_prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
+    print(f"Patient Response: {patient_response}")  # Debugging
     patient_tokens += len(patient_response.split())
     conversation.append({"role": "patient", "message": patient_response, "tokens": len(patient_response.split())})
 
     for turn in range(turns):
         # Physician's turn
         physician_prompt = f"As a physician, how would you respond to: {patient_response}"
+        print(f"Physician Turn {turn} Prompt: {physician_prompt}")  # Debugging
         physician_response = physician(physician_prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
+        print(f"Physician Response: {physician_response}")  # Debugging
         physician_tokens += len(physician_response.split())
         conversation.append({"role": "physician", "message": physician_response, "tokens": len(physician_response.split())})
 
         # Patient's turn
         patient_prompt = f"As a patient, how would you respond to: {physician_response}"
+        print(f"Patient Turn {turn} Prompt: {patient_prompt}")  # Debugging
         patient_response = patient(patient_prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
+        print(f"Patient Response: {patient_response}")  # Debugging
         patient_tokens += len(patient_response.split())
         conversation.append({"role": "patient", "message": patient_response, "tokens": len(patient_response.split())})
 
@@ -54,7 +60,7 @@ with gr.Blocks() as demo:
     gr.Markdown("## 👨‍⚕️ Synthetic Data Generation: Physician-Patient Role-Play 👤")
     with gr.Row():
         topic_input = gr.Textbox(label="Enter Disease/Topic", placeholder="e.g., chest pain")
-        turns_input = gr.Number(label="Number of Turns", value=5)
+        turns_input = gr.Number(label="Number of Turns", value=1)  # Default to 1 turn for debugging
     submit_button = gr.Button("🚀 Start Interaction")
     output_json = gr.JSON(label="Generated Conversation")
 
@@ -72,4 +78,4 @@ with gr.Blocks() as demo:
         outputs=output_json
     )
 
-demo.launch()
+demo.launch()